Implementation. We use DeepLabV3 [1]} with resnet-\(\lbrace 18,101\rbrace \)  [2]} backbones for semantic segmentation experiments. On Synthia, images are resized to 768 by 384. The resnet models are trained for 100 thousand iterations. On Cityscapes, images are resized to 769 by 769, and models are trained for 90 thousand iterations following [3]}. We use the Adam optimizer [4]} with a learning rate of \(10^{-3}\)  and \(10^{-4}\)  without annealing respectively. Note that better performance can be achieved with larger batch size and using SGD optimizer with a learning schedule. However, we focus on comparing relative performance of different loss functions under the same training hyperparameters for segmentation. On image classification, we follow the setting on previous works [5]} and use resnet-152 for Place-LT and resnet-50 for inaturalist2018. Specifically for Place-LT, we use SGD with a initial learning rate 0.005; for inaturalist2018, we use SGD with an initial learning rate 0.01. A batch size of 128 is used for both datasets.
